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pmid:34109210

Identifying Dendritic Cell-Related Genes Through a Co-Expression Network to Construct a 12-Gene Risk-Scoring Model for Predicting Hepatocellular Carcinoma Prognosis.

Chaoyuan Huang · Xiaotao Jiang · Yuancheng Huang · Lina Zhao · Peiwu Li · Fengbin Liu

Frontiers in molecular biosciences · 2021 doi:10.3389/fmolb.2021.636991

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title
Identifying Dendritic Cell-Related Genes Through a Co-Expression Network to Construct a 12-Gene Risk-Scoring Model for Predicting Hepatocellular Carcinoma Prognosis.
doi
10.3389/fmolb.2021.636991
pmid
34109210
authors array · 6 items
[0]
Chaoyuan Huang
[1]
Xiaotao Jiang
[2]
Yuancheng Huang
[3]
Lina Zhao
[4]
Peiwu Li
[5]
Fengbin Liu
journal
Frontiers in molecular biosciences
year
2021
abstract
The prognostic prediction of hepatocellular carcinoma (HCC) is still challenging. Immune cells play a crucial role in tumor initiation, progression, and drug resistance. However, prognostic value of immune-related genes in HCC remains to be further clarified. In this study, the mRNA expression profiles and corresponding clinical information of HCC patients were downloaded from public databases. Then, we estimated the abundance of immune cells and identified the differentially infiltrated and prognostic immune cells. The weighted gene co-expression network analysis (WGCNA) was performed to identify immune-related genes in TCGA cohort and GEO cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to establish a risk-scoring model in the TCGA cohort. HCC patients from the GSE14520 datasets were utilized for risk model validation. Our results found that high level of dendritic cell (DC) infiltration was associated with poor prognosis. Over half of the DC-related genes (58.2%) were robustly differentially expressed between HCC and normal specimens in the TCGA cohort. 17 differentially expressed genes (DEGs) were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. A 12-gene risk-scoring model was established to evaluate the prognosis of HCC. The high-risk group exhibits significantly lower OS rate of HCC patients than the low-risk group. The risk-scoring model shows benign predictive capacity in both GEO dataset and TCGA dataset. The 12-gene risk-scoring model may independently perform prognostic value for HCC patients. Receiver operating characteristic (ROC) curve analysis of the risk-scoring model in GEO cohort and TCGA cohort performed well in predicting OS. Taken together, the 12-gene risk-scoring model could provide prognostic and potentially predictive information for HCC. SDC3, NCF2, BTN3A3, and WARS were noticed as a novel prognostic factor for HCC.
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Based on the expression of overlapping genes with differential expression and prognostic value as well as survival data, the “glmnet” R package was applied for the LASSO Cox regression analysis to further select and shrink predictors.
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sections[9] "Construction and Validation of the Prognostic Immune Cell–Related Risk-Scoring Model" paragraph 2
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Based on the expression of overlapping genes with differential expression and prognostic value as well as survival data, the “glmnet” R package was applied for the LASSO Cox regression analysis to further select and shrink predictors.
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession numbers can be found below: https://www.ncbi.nlm.nih.gov/geo/, GSE14520 https://xenabrowser.net/datapages/, TCGA-LIHC. htseq_fpkm.tsv.
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sections[23] "Data Availability Statement" paragraph 2
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession numbers can be found below: https://www.ncbi.nlm.nih.gov/geo/, GSE14520 https://xenabrowser.net/datapages/, TCGA-LIHC. htseq_fpkm.tsv.
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession numbers can be found below: https://www.ncbi.nlm.nih.gov/geo/, GSE14520 https://xenabrowser.net/datapages/, TCGA-LIHC. htseq_fpkm.tsv.
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488
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3921;571
taxon
Homo sapiens
title
Gene expression data of human hepatocellular carcinoma (HCC)
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2026-11-01T15:39:54Z
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Clinical characteristics of the HCC patients used in this study.
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Data Collection and Preprocessing
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TABLE 1
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Clinical characteristics of the HCC patients used in this study.
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[1]
TCGA cohort
[2]
GEO cohort
[1] array · 3 items
[0]
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[1]
371
[2]
209
[2] array · 3 items
[0]
Age (median, range)
[1]
61 (16–90)
[2]
51 (21–77)
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[0]
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[1]
[2]
[4] array · 3 items
[0]
Female
[1]
120 (32.3%)
[2]
26 (12.4%)
[5] array · 3 items
[0]
Male
[1]
251 (67.7%)
[2]
183 (87.6%)
[6] array · 3 items
[0]
Race
[1]
[2]
[7] array · 3 items
[0]
White
[1]
185 (49.9%)
[2]
NA
[8] array · 3 items
[0]
Asian
[1]
158 (42.6%)
[2]
NA
[9] array · 3 items
[0]
Black or American
[1]
18 (4.8%)
[2]
NA
[10] array · 3 items
[0]
Unknown
[1]
10 (2.7%)
[2]
NA
[11] array · 3 items
[0]
Grade(%)
[1]
[2]
[12] array · 3 items
[0]
Grade 1
[1]
55 (14.8%)
[2]
NA
[13] array · 3 items
[0]
Grade 2
[1]
178 (50.0%)
[2]
NA
[14] array · 3 items
[0]
Grade 3
[1]
120 (32.3%)
[2]
NA
[15] array · 3 items
[0]
Grade 4
[1]
13 (3.5%)
[2]
NA
[16] array · 3 items
[0]
Unknown
[1]
5 (1.3%)
[2]
NA
[17] array · 3 items
[0]
Stage (%)
[1]
[2]
[18] array · 3 items
[0]
Ⅰ
[1]
174 (46.9%)
[2]
90 (43.1%)
[19] array · 3 items
[0]
Ⅱ
[1]
85 (22.9%)
[2]
74 (35.4%)
[20] array · 3 items
[0]
Ⅲ
[1]
84 (22.6%)
[2]
43 (20.6%)
[21] array · 3 items
[0]
Ⅳ
[1]
4 (1.1%)
[2]
NA
[22] array · 3 items
[0]
unknown
[1]
24 (6.4%)
[2]
2 (0.9%)
[23] array · 3 items
[0]
Child–Pugh(%)
[1]
[2]
[24] array · 3 items
[0]
A
[1]
222 (59.8%)
[2]
NA
[25] array · 3 items
[0]
B
[1]
21 (5.7%)
[2]
NA
[26] array · 3 items
[0]
C
[1]
1 (0.3%)
[2]
NA
[27] array · 3 items
[0]
unknown
[1]
127 (34.2%)
[2]
NA
[28] array · 3 items
[0]
Main tumor Size(%)
[1]
[2]
[29] array · 3 items
[0]
>5 cm
[1]
NA
[2]
75 (35.9%)
[30] array · 3 items
[0]
≤5 cm
[1]
NA
[2]
133 (63.6%)
[31] array · 3 items
[0]
Unknown
[1]
NA
[2]
1 (0.5%)
[32] array · 3 items
[0]
Survival status
[1]
[2]
[33] array · 3 items
[0]
OS days (median)
[1]
602
[2]
1,581
locator
section[3]/table-wrap[1]#T1
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asset:data:geo:GSE14520 not downloaded
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asset:data:geo:GSE14520 licence not read
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method-1
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cox_proportional_hazards
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quote
Based on the expression of overlapping genes with differential expression and prognostic value as well as survival data, the “glmnet” R package was applied for the LASSO Cox regression analysis to further select and shrink predictors.
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The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards.
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tier
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capability:computational_analysis
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The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
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tier
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession numbers can be found below: https://www.ncbi.nlm.nih.gov/geo/, GSE14520 https://xenabrowser.net/datapages/, TCGA-LIHC. htseq_fpkm.tsv.
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The paper names GSE14520 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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Based on the expression of overlapping genes with differential expression and prognostic value as well as survival data, the “glmnet” R package was applied for the LASSO Cox regression analysis to further select and shrink predictors.
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Immune cells play a crucial role in tumor initiation, progression, and drug resistance. However, prognostic value of immune-related genes in HCC remains to be further clarified. In this study, the mRNA expression profiles and corresponding clinical information of HCC patients were downloaded from public databases. Then, we estimated the abundance of immune cells and identified the differentially infiltrated and prognostic immune cells. The weighted gene co-expression network analysis (WGCNA) was performed to identify immune-related genes in TCGA cohort and GEO cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to establish a risk-scoring model in the TCGA cohort. HCC patients from the GSE14520 datasets were utilized for risk model validation. Our results found that high level of dendritic cell (DC) infiltration was associated with poor prognosis. Over half of the DC-related genes (58.2%) were robustly differentially expressed between HCC and normal specimens in the TCGA cohort. 17 differentially expressed genes (DEGs) were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. A 12-gene risk-scoring model was established to evaluate the prognosis of HCC. The high-risk group exhibits significantly lower OS rate of HCC patients than the low-risk group. The risk-scoring model shows benign predictive capacity in both GEO dataset and TCGA dataset. The 12-gene risk-scoring model may independently perform prognostic value for HCC patients. Receiver operating characteristic (ROC) curve analysis of the risk-scoring model in GEO cohort and TCGA cohort performed well in predicting OS. Taken together, the 12-gene risk-scoring model could provide prognostic and potentially predictive information for HCC. 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